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. 2018 Aug 20;9:3329. doi: 10.1038/s41467-018-05743-y

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Model validation. Validation of the thickness of the mobile regolith (TMR)-curvature (C) approach at Babbington Creek and Reynolds Mountain, Idaho (a) and Gordon Gulch, Colorado, USA (b). Solid white, gray, and black lines represent best-fit predicted vs. observed TMR values based on curvature calculated from a Light Detection and Ranging (LiDAR)-derived digital elevation model (DEM) and a single soil pit for Reynolds Mountain (c), Babbington Creek (d), and Gordon Gulch (e), respectively. Large dashed black lines represents the 1:1 predicted versus observed line. The best-fit slopes were not significantly different from one for Babbington Creek (|t| = 0.74 < critical t_0.05,5 = 2.571) and Reynolds Mountain (|t| = 0.01 < critical t_0.05,7 = 2.365), indicating unbiased models, whereas the slope was significantly lower than one for Gordon (p < 0.001, |t| = 3.64 < critical t_0.05,162 = 1.974), indicating over-prediction with higher TMR than observed. Small white, gray, and black dotted lines represent the 95% prediction intervals (PI)